Merge pull request #2 from aryan-mehta/master

Tensorflow Model for Human v/s Horse classification

Author: 99ansh

Classification_human_orr_horse.py

@@ -0,0 +1,46 @@
+import tensorflow as tf
+from tensorflow import keras
+import pickle
+model=tf.keras.models.Sequential([tf.keras.layers.Conv2D(16,(3,3),activation='relu',input_shape=(200,200,3)),
+                                  tf.keras.layers.MaxPooling2D(2,2),
+                                  tf.keras.layers.Conv2D(16,(3,3),activation='relu'),
+                                  tf.keras.layers.MaxPooling2D(2,2),
+                                  tf.keras.layers.Conv2D(16,(3,3),activation='relu'),
+                                  tf.keras.layers.MaxPooling2D(2,2),
+                                  tf.keras.layers.Flatten(),
+                                  tf.keras.layers.Dense(512,activation='relu'),
+                                  tf.keras.layers.Dense(1,activation="sigmoid")
+                                  ])
+model.summary()
+from tensorflow.keras.optimizers import RMSprop
+model.compile(optimizer=RMSprop(lr=0.001),loss='binary_crossentropy',metrics=['acc'])
+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+train_datagen=ImageDataGenerator(rescale=1/255)
+train_generator=train_datagen.flow_from_directory('E:/ARYAN/Desktop/python_tensorflow/Classification_human-or-horse',
+                                                  target_size=(200,200),
+                                                  batch_size=222,
+                                                  class_mode='binary')
+model.fit_generator(train_generator,steps_per_epoch=6,epochs=1,verbose=1)
+filename="myTf1.sav"
+pickle.dump(model,open(filename,'wb'))
+
+from tkinter import Tk
+from tkinter.filedialog import askopenfilename
+from keras.preprocessing import image
+import numpy as np
+import cv2
+
+Tk().withdraw() 
+filename = askopenfilename() 
+print(filename)
+img = image.load_img(filename, target_size=(200, 200))
+x = image.img_to_array(img)
+x = np.expand_dims(x, axis=0)
+images = np.vstack([x])
+classes = model.predict(images, batch_size=10)
+print(classes[0])
+if classes[0]>0.5:
+    print(filename + " is a human")
+else:
+    print(filename + " is a horse")
+